fast_tvb

Fast and parallel C implementation of TVB

Pull and run fast_tvb as a Docker container here

https://hub.docker.com/r/thevirtualbrain/fast_tvb

The code implements a brain network model composed of connected ReducedWongWang neural mass models (Wong & Wang, 2006) with feedback inhibition control (FIC). For more information on the model and FIC please see Deco et al. (2014), Schirner et al. (2018) and Shen et al. (2019).

For more information on The Virtual Brain (TVB) please see www.thevirtualbrain.org

For questions and other enquiries please contact

Michael Schirner (michael.schirner@charite.de) or Petra Ritter (petra.ritter@charite.de).

Generating the Docker container

If you want to generate the Docker container from scratch, two steps are necessary: first, compiling the C file, and, second, generating a Docker container with the new binary.

Step 1: Compile tvbii_multicore.c

We will use a Docker container to compile the C code, see folder step1_compile_C_code.

1. Make sure you have the Docker client installed and running (https://www.docker.com/products/docker-desktop)
2. Download/pull this repository onto your local machine.
3. Create a folder /path/to/tvb_binary where the compiled binary will be stored.
4. Open a command line shell and enter the directory step1_compile_C_code.
5. Run the following:

docker build -t tvb_c .
docker run --rm --mount type=bind,source=/path/to/tvb_binary,target=/output tvb_c /compile_and_copy.sh
cp /path/to/tvb_binary/tvb ../step2_create_Docker_container/
cat /path/to/tvb_binary/build_output.txt

The first command builds the Docker container with the build environment for compiling the C file. The second command compiles the C file and copies the output into the folder /path/to/tvb_binary in your local filesystem. The third command copies the created binary tvb into the folder step2_create_Docker_container. The fourth command lets you inspect the output of the compiler -- closely inspect whether it contains error messages or warning that need to be fixed.

Step 2: Create container for fast_tvb

6. Now that our fast_tvb binary is created, we will create a Docker container that contains the binary for convenient execution on different platforms. Enter the folder step2_create_Docker_container.

7. To build the Docker file run

docker build -t <your repo>/fast_tvb .

replacing <your repo> with the name of your Dockerhub repository.

8. To push it into your repository run

docker push <your repo>/fast_tvb

9. To run fast_tvb run

docker run --rm --mount type=bind,source=/path/to/step2_create_Docker_container/output,target=/output --mount type=bind,source=/path/to/step2_create_Docker_container/input,target=/input  <your repo>/fast_tvb /start_simulation.sh param_set.txt gavg 4

The folder step2_create_Docker_container contains a demo brain network model in the folder input.

For more information on how to use fast_tvb, please follow the instructions at https://hub.docker.com/r/thevirtualbrain/fast_tvb.

References

Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation–inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34(23), 7886-7898.

Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018). Inferring multi-scale neural mechanisms with brain network modelling. Elife, 7, e28927.

Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R., ... & Ritter, P. (2022). Brain simulation as a cloud service: The Virtual Brain on EBRAINS. NeuroImage, 118973.

Shen, K., Bezgin, G., Schirner, M., Ritter, P., Everling, S., McIntosh, A. R. (2019) A macaque connectome for large-scale network simulations in TheVirtualBrain. (under review)

Wong, K. F., & Wang, X. J. (2006). A recurrent network mechanism of time integration in perceptual decisions. Journal of Neuroscience, 26(4), 1314-1328.

Acknowledgments

This research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement Nos. 785907 (Human Brain Project SGA2), 945539 (Human Brain Project SGA3), ICEI 800858, VirtualBrainCloud 826421 and ERC 683049.